Data classification in a wireless communication system

ABSTRACT

A method of data classification for use in a wireless communication system includes obtaining decoder metrics from a decoder. The decoder metrics correspond to data generated by the decoder. The decoder metrics include a first metric and a second metric. The method also includes classifying the data into a first category if the data fails an error detection check, into a second category if the data passes the error detection check and is determined to be unreliable, or into a third category if the data passes the error detection check and is determined to be reliable. A reliability of the data is determined based on at least one of the decoder metrics and a threshold.

I. CLAIM OF PRIORITY AND CROSS-REFERENCE TO RELATED APPLICATION

The present application claims priority to U.S. Provisional ApplicationNo. 61/381,799 filed Sep. 10, 2010, which is incorporated by referenceherein in its entirety. The present application is related to U.S.patent application, Attorney Docket No. 102789U1, filed on the samedate, and titled “Data Classification Based On Cyclic Redundancy Checkand Decoder Metric,” which is incorporated by reference herein in itsentirety.

II. FIELD

The present disclosure is generally related to data processing in awireless communication system.

III. DESCRIPTION OF RELATED ART

Advances in technology have resulted in smaller and more powerfulcomputing devices. For example, there currently exist a variety ofportable personal computing devices, including wireless computingdevices, such as portable wireless telephones, personal digitalassistants (PDAs), and paging devices that are small, lightweight, andeasily carried by users. More specifically, portable wirelesstelephones, such as cellular telephones and internet protocol (IP)telephones, can communicate voice and data packets over wirelessnetworks. Further, many such wireless telephones include other types ofdevices that are incorporated therein. For example, a wireless telephonecan include a digital still camera, a digital video camera, a digitalrecorder, and an audio file player. Also, such wireless telephones canprocess executable instructions, including software applications, suchas a web browser application, that can be used to access the Internet.As such, these wireless telephones can include significant computingcapabilities.

Such computing devices may include a receiver that operates according toa high-speed uplink packet access (HSUPA) protocol. HSUPA is a featureof 3rd Generation Partnership Project (3GPP) Release 6 that allows forincreased data rates, lower scheduling delays, and reduced latency ofuplink data. In wireless systems that implement the HSUPA protocol, anenhanced dedicated channel (E-DCH) is used to carry uplink data fromuser equipment (UE) (e.g., a wireless telephone) to a NodeB (e.g., abase station). Data rate and power of E-DCH channels are controlled by aNodeB partly by means of absolute grants transmitted to UEs over adownlink physical layer channel, E-AGCH (E-DCH absolute grant channel).To transmit an absolute grant to UEs, an error detection code (e.g., acyclic redundancy check (CRC) code) is appended to grant-and-scope data.The combined grant-and-scope data and error detection code is encodedand punctured to produce a codeword. The codeword is then transmitted onthe E-AGCH physical layer channel to UEs served by the NodeB.

The UEs may receive and decode signals carried over the E-AGCH andperform error detection (e.g., a CRC check) on the decoded data forevery transmission time interval (TTI). However, the NodeB may nottransmit an absolute grant in each TTI. When there is no absolute granttransmitted by the NodeB in a particular TTI, the data decoded by theUEs during that TTI is not valid (e.g., random data). However, there isa possibility that an error detection will indicate the decoded data iserror-free (e.g., a CRC pass) even though no valid data was received. Afalse error-free data indication (i.e., a “false alarm”) may cause anerroneous determination by a UE that an absolute grant was transmitted.E-AGCH false alarms may therefore have an adverse impact on networkthroughput and stability.

IV. SUMMARY

Data classification for use in a wireless communication system includesclassifying decoded data based on decoder metrics and an error detectioncheck. The decoder metrics may be used to determine a reliability of thedata, and error detection information may be used to determine whetherthe decoded data passes or fails an error detection check, such as acyclic redundancy check (CRC). The data may be classified based on adetermined reliability of the data and a result of the error detectioncheck. For example, the data (and the corresponding received signal) maybe classified as having passed CRC but determined to be unreliable. Oneor more parameters used to classify the data based on the decodermetrics may be updated for a subsequent classification.

In a particular embodiment, a method of data classification for use in awireless communication system includes obtaining decoder metrics from adecoder. The decoder metrics correspond to data generated by the decoderand include a first metric and a second metric. The method includesclassifying the data into a first category if the data fails an errordetection check, into a second category if the data passes the errordetection check and is determined to be unreliable, or into a thirdcategory if the data passes the error detection check and is determinedto be reliable. A reliability of the data is determined based on thedecoder metrics and a threshold.

In another particular embodiment, a method of data classification foruse in a wireless communication system includes obtaining decodermetrics from a decoder. The decoder metrics correspond to data generatedby the decoder and include an energy metric (EM) and a symbol error rate(SER). The method includes classifying the data into a first category ifthe data fails a cyclic redundancy check (CRC) check, into a secondcategory if the data passes the CRC check and is determined to beunreliable, or into a third category if the data passes the CRC checkand is determined to be reliable. A reliability of the data isdetermined based on the decoder metrics and an EM threshold.

In a particular embodiment, a device for data classification for use ina wireless communication system includes logic to obtain decoder metricsfrom a decoder. The decoder metrics correspond to data generated by thedecoder and include a first metric and a second metric. The deviceincludes a classifier to classify the data into a first category if thedata fails an error detection check, to classify the data into a secondcategory if the data passes the error detection check and is determinedto be unreliable, and to classify the data into a third category if thedata passes the error detection check and is determined to be reliable.A reliability of the data is determined based on the decoder metrics anda threshold.

In another particular embodiment, a device for data classification foruse in a wireless communication system includes logic to obtain decodermetrics from a decoder. The decoder metrics correspond to data generatedby the decoder and include an energy metric (EM) and a symbol error rate(SER). The device includes a classifier to classify the data into afirst category if the data fails a cyclic redundancy check (CRC) check,into a second category if the data passes the CRC check and isdetermined to be unreliable, or into a third category if the data passesthe CRC check and is determined to be reliable. A reliability of thedata is determined based on the decoder metrics and an EM threshold.

One particular advantage provided by at least one of the disclosedembodiments is that random or erroneous data may be identified asunreliable even though the data passes the error detection check. Forexample, random data that passes a CRC check may be identified asunreliable. Such unreliable data may be classified accordingly andsubsequently discarded or processed in a manner appropriate for dataclassified into a corresponding category.

Other aspects, advantages, and features of the present disclosure willbecome apparent after review of the entire application, including thefollowing sections: Brief Description of the Drawings, DetailedDescription, and the Claims.

V. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a particular embodiment of a wirelesscommunication system that includes a device configured to perform dataclassification;

FIG. 2 is a block diagram of another particular embodiment of thewireless communication system of FIG. 1 including another particularembodiment of the device 104 of FIG. 1;

FIG. 3 is a flow chart of a particular embodiment of a method of dataclassification for use in a wireless communication system;

FIG. 4 is a diagram of a particular illustration of various reliabilityregions corresponding to decoded data.

FIG. 5 is an illustrative example of the various reliability regionsillustrated with respect to FIG. 4.

FIG. 6 is a flow chart of a particular embodiment of a method of dataclassification for use in a wireless communication system;

FIG. 7 is a flow chart of a particular illustrative embodiment of amethod of data classification for use in a wireless communicationsystem; and

FIG. 8 is a block diagram of wireless device that can perform dataclassification for use in a wireless communication system.

VI. DETAILED DESCRIPTION

Referring to FIG. 1, a particular illustrative embodiment of a wirelesscommunication system to enable signal classification is depicted andgenerally designated 100. The wireless communication system 100 includesa wireless network element 102 and a device 104 configured to performdata classification for use in the wireless communication system 100.The wireless network element 102 may be a NodeB or a base station. Toillustrate, the wireless network element 102 may communicate with thedevice 104 over a radio network. For example, the wireless networkelement 102 may transmit a downlink signal 112 to the device 104.Similarly, the device 104 may transmit an uplink signal 114 to thewireless network element 102. In a particular embodiment, the device 104may be a portable communication device, such as a mobile phone, asmartphone, a laptop computer, a tablet computer, a personal digitalassistant (PDA), a portable media player, another portable electronicdevice operable to perform wireless communication, or any combinationthereof.

In a particular embodiment, the device 104 is configured to classifydata into categories and to indicate a classification 110 of the data120. The device 104 may include a decoder 106 and a classifier 108. Thedecoder 106 is configured to receive a signal transmitted by thewireless network element 102. To illustrate, the decoder 106 may receivethe downlink signal 112 from the wireless network element 102. Thedecoder 106 is further configured to decode the received signal and togenerate decoded output data. For example, the decoder 106 may generatedata 120 by decoding the received signal. In a particular embodiment,the decoder 106 may be a Viterbi decoder for convolutional codes.

The decoder 106 may be configured to generate error detectioninformation 122 that corresponds to the data 120. The error detectioninformation 122 may include an error detection code that is decoded froma received signal, a result of an error detection check performed by thedecoder on the data 120, or both. For example, the error detectioninformation 122 may include a cyclic redundancy check (CRC) code that isdecoded by the decoder 106 from the downlink signal 112. The errordetection code provided by the decoder 106 may be used by the classifier108 to perform an error detection check on the data 120. Alternatively,the error detection information 122 may include a result of an errordetection check performed by the decoder 106 or another component of thedevice 104. For example, the error detection check may be performedbased on a CRC code. In a particular embodiment, the CRC code may be a16-bit CRC code.

In a particular embodiment, the decoder 106 is configured to generatedecoder metrics 124 corresponding to the data 120. The decoder metrics124 may include a first metric and a second metric. The first metric maycorrespond to a scale invariant metric and the second metric maycorrespond to a scale variant metric. For example, the first metric mayinclude a symbol error rate (SER), and the second metric may include anenergy metric (EM), such as a correlation energy metric. The decodermetrics 124 may be used by the classifier 108 to determine a reliabilityof the data.

The classifier 108 is configured to classify the data 120 based on areliability of the data 120 and a result of an error detection checkperformed on the data 120. The reliability of the data 120 may bedetermined based on the decoder metrics 124 and a metric threshold. Forexample, the classifier 108 may include logic 116 to obtain decodermetrics 124 from a decoder 106. The reliability of the data 120 may bedetermined based on the decoder metrics 124 and an EM threshold, asdescribed with respect to FIGS. 3-5. To illustrate, the classifier 108may determine the data 120 to be unreliable if a first metric of thedecoder metrics 124 satisfies a first threshold. For example, the data120 is determined to be unreliable if the SER satisfies a first SERthreshold. The SER may satisfy the first threshold if the SER exceedsthe first SER threshold. The classifier 108 may also determine the data120 to be unreliable if the first metric of the decoder metrics 124satisfies a second threshold and a second metric of the decoder metrics124 fails to satisfy the metric threshold. For example, the data 120 maybe determined to be unreliable if the SER satisfies a second SERthreshold and an EM fails to satisfy an EM threshold. The SER maysatisfy the second SER threshold if the SER exceeds the secondthreshold. The EM may fail to satisfy the EM threshold if the EM isbelow the EM threshold.

In a particular embodiment, the classifier 108 may receive errordetection information 122 including a result of an error detection checkperformed by the decoder 106. For example, the error detectioninformation 122 may include a result of a CRC check performed by thedecoder 106. In another embodiment, the error detection information 122may include an error detection code corresponding to the data 120. Whenthe error detection information 122 includes an error detection code,the classifier 108 may perform an error detection check on the data 120based on the error detection code. For example, the classifier 108 mayperform a CRC check on the data 120 based on a CRC code received fromthe decoder 106.

In a particular embodiment, the classifier 108 may classify the data 120into one of three categories and indicate a classification 110. Toillustrate, the classifier 108 may classify the data 120 into a firstcategory if the data 120 fails an error detection check. The classifier108 may classify the data 120 into a second category if the data 120passes the error detection check and is determined to be unreliable. Theclassifier 108 may classify the data 120 into a third category if thedata 120 passes the error detection check and is determined to bereliable. For example, the classifier 108 may classify the data 120 intothe first category if the data 120 fails a cyclic redundancy check (CRC)check, into the second category if the data 120 passes the CRC check andis determined to be unreliable, or into the third category if the data120 passes the CRC check and is determined to be reliable.

During operation, the device 104 may receive a signal from the wirelessnetwork element 102. The decoder 106 may decode the received signal andgenerate the data 120. The decoder 106 may also generate the errordetection information 122 corresponding to the data 120. For example,the decoder 106 may perform an error detection check (e.g., a CRC check)on the data 120 or on other data corresponding to the data 120 and maygenerate a result of the error detection check, such as a result of aCRC check. The decoder 106 may also generate the decoder metrics 124including a first metric and a second, such as an SER and an EM,corresponding to the data 120.

The classifier 108 may receive the data 120, the error detectioninformation 122, and the decoder metrics 124 from the decoder 106. Theclassifier 108 may determine a reliability of the data 120 based on thedecoder metrics 124 and a metric threshold, such as an EM threshold. Forexample, the classifier 108 may determine the data 120 to be unreliableif the first metric satisfies (e.g., exceeds) a first threshold. Theclassifier 108 may also determine the data 120 to be unreliable if thefirst metric satisfies a second threshold and a second metric fails tosatisfy the metric threshold.

The classifier 108 may classify the data 120 into the first category ifthe result of the error detection check indicates the data 120 failedthe error detection check. The classifier 108 may classify the data 120into the second category if the result of an error detection checkindicates the data 120 passed the error detection check but classifier108 determines the data 120 to be unreliable. Additionally, theclassifier 108 may classify the data 120 into the third category if theresult of the error detection check indicates the data 120 passed theerror detection check and the classifier 108 determined the data 120 tobe reliable (i.e., the data is not associated with characteristicscorresponding to unreliable data, such as high SER and/or low EM.

By classifying the data 120 into categories and indicating theclassification 110, the data 120 may be processed in a manner that isappropriate to each category. For example, the data 120 that isclassified into the first category (e.g., the fail category) may bediscarded without further processing. The data 120 that is classifiedinto the second category (e.g., the false CRC pass category) may bediscarded without further processing or, alternatively, may be furtherprocessed in another manner. The data 120 that is classified into thethird category (e.g., the pass category) may be transferred tosubsequent communication protocol layers.

Referring to FIG. 2, a particular illustrative embodiment of thewireless communication system of FIG. 1 is depicted and generallydesignated 200. In a particular embodiment, the wireless communicationsystem 200 may implement a high-speed uplink packet access (HSUPA)protocol, which is a 3rd Generation Partnership Project (3GPP) Release 6feature that allows for increased data rates, lower scheduling delays,and reduced latency of uplink data. The wireless communication system200 includes the wireless network element 102 and the device 104configured to perform data classification for use in the wirelesscommunication system 200. For example, the device 104 may transmit anuplink signal to the wireless network element 102 over an enhanceddedicated channel (E-DCH) 216. Similarly, the wireless network element102 may transmit a downlink signal to the device 104 over an E-DCHabsolute grant channel (E-AGCH) 214. To illustrate, E-AGCH 214 iscompliant with the HSUPA protocol.

The device 104 includes the decoder 106, the classifier 108, and athreshold updater 212. The decoder 106 is configured to decode the data120 from a signal carried on the E-AGCH 214. For example, the signal maybe carried in an E-AGCH frame. Alternatively, the signal may be carriedin an E-AGCH subframe. The decoder 106 may be configured to generate thedata 120, the error detection information 122 corresponding to the data120, and the decoder metrics 124 as described with respect to FIG. 1.

In a particular embodiment, the data 120 decoded from a received signalmay be associated with a transmission time interval (TTI). Toillustrate, a signal that is carried in an E-AGCH frame may correspondto a 10 millisecond TTI, and a signal that is carried in an E-AGCHsubframe may correspond to a 2 millisecond TTI. The decoder metrics 124and the data 120 may also be associated with the same TTI. For example,the decoder metrics 124 and the data 120 may be associated with the same10 millisecond TTI. Alternatively, the data 120 may be associated with acurrent TTI and the decoder metrics 124 may be associated with a priorTTI.

As described with respect to FIG. 1, the classifier 108 may use thedecoder metrics 124 and a metric threshold to determine a reliability ofthe data 120. The metric threshold may be initialized to a firstthreshold value before determining the reliability of the data 120. Toillustrate, the data 120 may be determined to be unreliable if the firstmetric satisfies a first threshold. The data 120 may also be determinedto be unreliable if the first metric satisfies a second threshold andthe second metric fails to satisfy a metric threshold.

For example, the first metric may be an SER and the second metric may bean EM. The SER, the EM, and the data 120 may correspond to the same TTI.The data 120 may be determined to be unreliable if the SER satisfies afirst SER threshold. The data 120 may also be determined to beunreliable if the SER satisfies a second SER threshold and the EM failsto satisfy an EM threshold. The SER may satisfy the first threshold ifthe SER exceeds the first SER threshold. Similarly, the SER may satisfythe second SER threshold if the SER exceeds the second SER threshold.The EM may fail to satisfy the EM threshold if the EM fails to exceedthe EM threshold.

The EM metric may be initialized to a first threshold value beforedetermining the reliability of the data 120. For example, the EMthreshold may be initialized when a new uplink call is establishedbetween the device 104 and the wireless network element 102. The EMthreshold may also be initialized when an uplink serving cell associatedwith the device 104 changes. In a particular embodiment, the first SERthreshold, the second SER threshold, and the EM threshold may bedetermined and optimized statically using simulation results orlaboratory/field results.

In a particular embodiment, the classifier 108 may receive errordetection information 122 including a result of an error detection checkperformed by the decoder 106. Alternatively, the classifier 108 mayreceive an error detection code corresponding to the data 120. Forexample, the classifier 108 may receive from the decoder 106 a result ofa CRC check corresponding to the data 120 or may receive a CRC codecorresponding to the data 120. When the classifier 108 receives a CRCcode from the decoder, the classifier 108 may perform a CRC check on thedata 120 based on the CRC code. The result of a CRC check received fromthe decoder 106 or determined by the classifier 108 may indicate whetherthe data 120 passes or fails the CRC check.

The classifier 108 may classify the data 120 into one of threecategories in a similar manner as described with respect to FIG. 1 andoutput an indication of the classification 110. For example, theclassifier 108 may classify the data 120 into the first category if thedata 120 fails a CRC check, into the second category if the data 120passes the CRC check and is determined to be unreliable, or into thethird category if the data 120 passes the CRC check and is determined tobe reliable. In a particular embodiment, the first category correspondsto a fail category, the second category corresponds to a false CRC passcategory, and the third category corresponds to a pass category.

In a particular embodiment, the threshold updater 212 is configured toreceive classification information 222 including the reliability of thedata 120 and the decoder metrics 124 from the classifier 108. Based onthe classification information 222, the threshold updater 212 may updatethe metric threshold (e.g., EM threshold). The threshold updater 212 mayset the metric threshold to a first threshold value in response todetermining, by the classifier 108, that the data is unreliable. Thethreshold updater 212 may update the metric threshold to a first updatethreshold value based on the first metric (e.g., SER) and a lowthreshold (e.g., a low SER threshold). The threshold updater 212 mayalso update the metric threshold to a second update threshold valuebased on the first metric (e.g., SER) and a high threshold (e.g., highSER threshold).

For example, the first metric may be an SER, the second metric may be anEM, and the metric threshold may be an EM threshold. The EM thresholdmay be set to a first threshold value (e.g., set to zero) in response toclassifying the data 120 into a fail category. The EM threshold may alsobe set to a first threshold value (e.g., set to zero) in response toclassifying the data 120, by the classifier 108, into a false CRC passcategory (which corresponds to the data 120 that passed a CRC check butwas found to be unreliable in a current TTI). The EM threshold may beupdated to a first update threshold value in response to determiningthat the SER fails to satisfy a low SER threshold. The SER may fail tosatisfy the low SER threshold if the SER is below the low SER threshold.The EM threshold may be updated to a second update threshold value inresponse to determining that the SER satisfies a high SER threshold. TheSER may satisfy the high SER threshold if the SER exceeds the high SERthreshold. The first update threshold value may be determined based on amultiplicative coefficient and an infinite impulse response (IIR)coefficient. The second update threshold value may be determined basedon the infinite impulse response (IIR) coefficient, the multiplicativecoefficient, and the EM. In a particular embodiment, the IIR coefficientis between 0 and 1.

During operation, the device 104 may receive a signal, such as a signalcarried in an E-AGCH frame, from the wireless network element 102. Thedecoder 106 may decode the signal and generate data 120 and decodermetrics 124. For example, the decoder metrics 124 may include a scaleinvariant metric (e.g., SER) and a scale variant metric (e.g., EM). Thedecoder 106 may also generate error detection information 122corresponding to the data 120. For example, the decoder 106 may performa CRC check on the data 120 or on other data corresponding to the data120 and generate a result of the CRC check.

The classifier 108 may receive the data 120, the error detectioninformation 122, and the decoder metrics 124 from the decoder 106. Theclassifier 108 may determine a reliability of the data 120 based on thedecoder metrics 124 and a metric threshold (e.g., an EM threshold). Theclassifier 108 may classify the data 120 in each TTI into one of threecategories. For example, the classifier 108 may classify the data 120into a fail category if a result of a CRC check indicates the data 120failed the CRC check. The classifier 108 may classify the data 120 intoa false CRC pass category if a result of a CRC check indicates the data120 passed the CRC check and the classifier 108 determines the data 120to be unreliable. The classifier 108 may classify the data 120 into apass category if a result of a CRC check indicates the data 120 passedthe CRC check and the classifier 108 determined the data 120 to bereliable.

An illustrative operation of the classifier 108 is further describedbelow by means of a pseudo-code. The pseudo-code may run in each TTIwith the EM, SER, and CRC result corresponding to a particular TTI. Inthe pseudo-code, THRESHOLD maybe initialized to 0 when an enhanceduplink (EUL) call is established between the device 104 and the wirelessnetwork element 102 or when an EUL serving cell changes. Further, theinitialization of THRESHOLD or setting THRESHOLD to 0 may be performedby the classifier 108 or the threshold updater 210.

IF (CRC FAIL)    {STATUS = FAIL ;} ELSE IF (SER > SER_HIGH_STATUS)   {STATUS = FALSE_CRC_PASS; THRESHOLD = 0;}    (Unreliable) ELSE IF((SER > SER_LOW_STATUS) AND (EM < THRESHOLD)    {STATUS =FALSE_CRC_PASS; THRESHOLD = 0;}    (Unreliable) ELSE    {STATUS = PASS;}(Reliable)

If the data 120 is classified in a pass category (e.g., STATUS=PASS inthe above pseudo-code), the threshold updater 212 may update the metricthreshold (e.g., the EM threshold) based on the first metric (e.g., SER)and high/low thresholds related to the first metric. The thresholdupdater 210 may increase the metric threshold if the first metriccorresponding to a current TTI is above a high threshold. The thresholdupdater 210 may reduce the metric threshold if the first metriccorresponding to the current TTI is below a low threshold. For example,the threshold updater may lower the EM threshold (i.e., reduce relianceon the EM) for checking reliability of data in a next TTI when the SERassociated with the current TTI is below a low SER threshold. Thethreshold updater may increase the EM threshold (i.e., increase relianceon the EM) for checking reliability of data in a next TTI when the SERassociated with the current TTI is above a high SER threshold. Anillustrative operation of the threshold updater 212 is described belowby means of a pseudo-code. The pseudo-code may run in each TTI.

IF (SER < SER_LOW_THRESHOLD)    {THRESHOLD = THRESHOLD*α} ELSE IF (SER >SER_HIGH_THRESHOLD)    {THRESHOLD = THRESHOLD*α + ζ*EM*(1− α)}

In the above pseudo-code of the illustrative operation of the thresholdupdater 212, the following parameters and illustrative values may beused:

-   -   α is the IIR filter coefficient satisfying 0<α<1, (e.g. a may be        0.1)    -   ζ is an empirically determined multiplicative coefficient, (e.g.        ζ may be 1.3)    -   SER_HIGH_STATUS=16    -   SER_LOW_STATUS=8    -   SER_HIGH_THRESHOLD=13    -   SER_LOW_THRESHOLD=8

Another illustrative operation of the classifier 108 is furtherdescribed below by means of a pseudo-code. The pseudo-code may run ineach TTI with the EM, SER, CRC result corresponding to a particular TTI.In the pseudo-code, SCALE may be initialized to 0 when an enhanceduplink (EUL) call is established or when an EUL serving cell changes.Further, initialization of SCALE or setting SCALE to 0 may be performedby the classifier 108 or the threshold updater 210.

THRESHOLD = ζ*SCALE IF (CRC FAIL)    {STATUS = FAIL ;} ELSE IF (SER >SER_HIGH_STATUS)    {STATUS = FALSE_CRC_PASS; SCALE = 0;} (Unreliable)ELSE IF ((SER > SER_LOW_STATUS) AND (EM < THRESHOLD)    {STATUS =FALSE_CRC_PASS; SCALE = 0;} (Unreliable) ELSE    {STATUS = PASS;}(Reliable)

Another illustrative operation of the threshold updater 212 is describedbelow by means of a pseudo-code. The pseudo-code may run in each TTI.

IF (SER < SER_LOW_SCALE)    {SCALE = SCALE*α} ELSE IF (SER >SER_HIGH_SCALE)    {SCALE = SCALE*α + EM*(1− α)}

In the above pseudo-code of the illustrative operation of the thresholdupdater 212, the following parameters and illustrative values may beused:

-   -   α is the IIR filter coefficient satisfying 0<α<1, (e.g. a may be        0.9)    -   ζ is an empirically determined multiplicative coefficient, (e.g.        ζ may be 1.3)    -   SER_HIGH_STATUS=16    -   SER_LOW_STATUS=8    -   SER_HIGH_SCALE=13    -   SER_LOW_SCALE=8

By classifying the data 120 into categories, the data 120 may beprocessed in a manner that is appropriate to a category. For example,the data 120 that is classified into the second category (e.g., thefalse CRC pass category) may be discarded without further processing or,alternatively, may be further processed in another manner. Identifyingdata that is erroneously identified as passing a CRC check may enable areduction of false alarms. To illustrate, identifying random datareceived over an E-AGCH may reduce a number of false grants that areerroneously recognized by UEs as actual grants transmitted by a basestation.

Referring to FIG. 3, a particular illustrative embodiment of a method ofdata classification for use in a wireless communication system isillustrated. The method 300 includes receiving a signal from a NodeB ora base station, at 302. As illustrated in FIG. 1, the device 104 mayreceive a downlink signal, such as the downlink signal 112, from thewireless network element 102. The wireless network element 102 may be aNodeB or a base station. Similarly, a signal carried over an E-AGCH 214of FIG. 2 may be received by the device 104.

The method 300 further includes decoding the signal and generatingdecoded data and decoder metrics SER and EM, at 304. For example, thedecoder 106 of FIGS. 1 and 2 may decode the signal received from thewireless network element 102 and generate data 120. The decoder 106 mayalso generate decoder metrics 124 including SER and EM.

A CRC check is performed on the decoded data, at 306. To illustrate, thedecoder 106 or the classifier 108 of FIGS. 1 and 2 may perform a CRCcheck on the data 120 generated by the decoder 106. When the decoder 106performs the CRC check, the decoder 106 may provide the result of theCRC check to the classifier 108. A determination is made whether thedecoded data passed or failed the CRC check, at 308. For example, theclassifier 108 of FIGS. 1 and 2 may determine whether the data 120passed or failed a CRC check based on the result of the CRC check.

In response to determining that the decoded data failed the CRC check,at 308, the decoded data is classified as “fail”, at 314. To illustrate,the classifier 108 of FIGS. 1 and 2 may classify the data 120 into afail category in response to determining that the data 120 failed theCRC check. Otherwise, in response to determining that the decoded datapassed the CRC check, at 308, a determination is made, at 310, whetherSER satisfies a first SER threshold. The classifier 108 may determinethat the data 120 is unreliable if the SER satisfies the first SERthreshold. For example, the classifier 108 may determine that the SERsatisfies the first SER threshold if the SER exceeds the first SERthreshold.

In response to determining that the SER fails to satisfy the first SERthreshold, at 310, a determination is made, at 312, whether the SERsatisfies a second SER threshold and EM fails to satisfy an EMthreshold. The classifier 108 may determine that the data 120 isunreliable if the SER satisfies the second SER threshold and the EMfails to satisfy the EM threshold. The classifier 108 may determine thatthe SER satisfies the second SER threshold if the SER exceeds the secondSER threshold. Similarly, the classifier 108 may determine that the EMfails to satisfy the EM threshold if the EM is below the EM threshold.The classifier 108 may determine that the data 120 is reliable if theSER fails to satisfy the second SER threshold and the EM satisfies theEM threshold.

In response to determining that the SER fails to satisfy the second SERthreshold and the EM satisfies the EM threshold, at 312, the decodeddata may be classified as “pass,” at 316. To illustrate, the classifier108 of FIGS. 1 and 2 may classify the data 120 into a pass category inresponse to determining that the data 120 passed the CRC check and thedata 120 is reliable. In response to determining that the data isunreliable, at 310 and at 312, the decoded data may be classified as a“false CRC pass,” at 318. For example, the classifier 108 of FIGS. 1 and2 may classify the data 120 into a false CRC pass category in responseto determining that the data 120 passed the CRC check but is unreliable.The method 300 also includes updating the EM threshold, at 320, asdescribed with respect to FIG. 2. For example, the threshold updater 210may update the EM threshold based on SER and EM.

The method 300 of FIG. 3 may be implemented by a processing unit such asa central processing unit (CPU), a digital signal processor (DSP), acontroller, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA) device, another hardware device, afirmware device, or any combination thereof. As an example, the methodof FIG. 3 can be performed by or in response to signals or commands froma processor that executes instructions, as described with respect toFIG. 6.

Referring to FIG. 4, a particular illustration of various reliabilityregions corresponding to decoded data is shown. For example, the datamay be decoded by a Viterbi decoder. Accordingly,

let {x_(i)}_(i=1) ^(N), x_(i)εR represent an input sequence to a Viterbidecoder;let {b_(i)}_(i=1) ^(M), b_(i)ε{−1, +1} represent a decoded output; andlet {e_(i)}_(i=1) ^(N), e_(i)ε{−1, +1} represent a re-encoded codewordobtained by encoding {b_(i)}as {b_(i)} would be encoded at a transmitter.

In addition to the decoded codeword, the Viterbi decoder may beconfigured to generate one or more metrics. These metrics are functionsof the input sequence and a given metric may be denoted as M({x_(i)}).

Two classes of metrics (i.e., scale variant metrics and scale invariantmetrics) may be considered. Scale variant metrics satisfy the followingcriteria:

M({x _(i)})≠M({αx _(i)})

Examples of scale variant metrics are:

Correlation energy metric (EM): E=Σe_(i)x_(i);Moments of order j: m_(j)=Σ|x_(i)|^(j);A₀: Viterbi path metric at state 0 at the end of the Viterbi decodingoperation as specified in Sec A.1.2 of 3GPP Technical Specification (TS)25.212;A_(max): Maximum Viterbi path metric among all survivors at the end ofthe Viterbi decoding operation as specified in Sec A.1.2 of 3GPP TS25.212; andA_(min): Minimum Viterbi path metric among all survivors at the end ofthe Viterbi decoding operation as specified in Sec A.1.2 of 3GPP TS25.212.

Scale invariant metrics satisfy the following criteria:

M({x _(i)})=M({αx _(i)})

Examples of scale invariant metrics are:

Symbol Error Rate (SER): Σ_(i=1) ^(N)

(x_(i) e_(i)), where

${(y)} = \{ \begin{matrix}{{1\mspace{14mu} {if}\mspace{14mu} y} < 0} \\{{{0\mspace{14mu} {if}\mspace{14mu} y} \geq 0};}\end{matrix} $

${S = {10\; \log_{10}\frac{A_{0} - A_{\min}}{A_{\max} - A_{\min}}}},$

as defined in Sec A.1.2 of 3GPP TS 25.212; and

$P = {{- 10}\; \log_{10}{\frac{E}{\sqrt{m_{2} - m_{1}^{2}}}.}}$

FIG. 4 illustrates a two-dimensional space formed by values of a scalevariant metric M_(S) 402 and a scale invariant metric M_(I) 404. A firstregion B₀ 406 includes values of M_(S) 402 and M_(I) 404, where afunction of M_(S) 402 and M_(I) 404 satisfy a criterion. To illustrate,Bo 406 includes values of ((M_(S) 402)/SCALE) and M_(I) 404 thatcorrespond to an unreliable decode. A second region B₁ includes valuesof M_(I) 404 that correspond to a low probability of unreliable decode.A third region B₂ includes values of M_(I) 404 that correspond to a highprobability of unreliable decode. “SCALE” is an adaptive scale factorthat may be updated based on at least one of the decoder metrics. Aparticular embodiment of a method of data classification based on thescale variant metric M_(S) 402 and the scale invariant metric M_(I) 404is described below by means of a pseudo-code:

1. Initialize adaptive scale factor SCALE: Initialize SCALE to 0 when anenhanced uplink (EUL) call is established, serving cell changes, or whencertain relevant physical configurations are updated (e.g., switching areceive antenna on or off). 2. Classify as PASS, FAIL, FALSE_CRC_PASS:IF (CRC FAIL) { STATUS = FAIL ;}$ {{{ELSE}\mspace{14mu} {IF}\mspace{14mu} ( {{SCALE} \neq 0} )\mspace{14mu} {AND}\mspace{14mu} ( {\frac{M_{S}}{SCALE},M_{I}} )} \in B_{0}} )${STATUS = FALSE_CRC_PASS; SCALE = 0;} ELSE {STATUS = PASS;} 3. Scaleadaptation: IF (M_(I) ε B₁) {SCALE = SCALE*α} ELSE IF (M_(I) ε B₂){SCALE = SCALE*α + M_(S)*(1− α)}

In the above described method,

₀ 406,

₁ 408, and

₂ 410 may be determined and optimized statically via simulations orbased on laboratory and/or field results. The adaptive scale factorSCALE may be updated based on at least one of the decoder metrics. Forexample, the adaptive scale factor SCALE is updated based on the scalevariant metric M_(S) 402 in the above pseudo-code.

In an illustrative example,

₀ 406 represents the region in the two-dimensional space of

$( {\frac{M_{S}}{SCALE},M_{I}} )$

that corresponds to an unreliable decode.

₁ 408 represents the region in the one dimensional space of M_(I) 404that corresponds to a high LLR of genuine transmission. LLR is the loglikelihood ratio and may be defined as the log of the ratio of theprobability of a genuine transmission to the probability of the absenceof a genuine transmission, where both probabilities may be conditionedon the observation of M_(I) 404.

₂ 410 represents the region in the one dimensional space of M_(±) 404that corresponds to a low LLR of genuine transmission.

Referring to FIG. 5, a particular illustration of the variousreliability regions illustrated with respect to FIG. 4 is provided. Thescale variant metric M_(S) 402 of FIG. 4 is represented by EM 502, andthe scale invariant metric M_(I) 404 of FIG. 4 is represented by SER504. The region B₀ 506 on the right side of the graph line 512represents a two-dimensional space that corresponds to unreliable decodeof data. The region B₁ 508 represents a region in a one-dimensionalspace of the scale invariant metric SER 504. The region B₁ 508 maycorrespond to a high LLR of genuine transmission. For example, theregion B₁ 508 may correspond to SER values of less than 8.

The region B₂ 510 represents another region in the one-dimensional spaceof the scale invariant metric SER 504. The region B₂ 510 may correspondto a low LLR of genuine transmission. For example, the region B₂ 510 maycorrespond to SER values of greater than 13. To illustrate, decoded datawith an associated SER in region B₂ 510 is likely to be less reliablethan decoded data that is associated with SER in region B₁ 508.

Referring to FIG. 6, a particular illustrative embodiment of a method ofdata classification for use in a wireless communication system isillustrated. The method 600 includes obtaining decoder metrics from adecoder, at 602. For example, the logic 116 of FIGS. 1 and 2 may obtainthe decoder metrics 124 from the decoder 106. The decoder metricscorrespond to data generated by the decoder. For example, the decodermetrics 124 of FIGS. 1 and 2 may correspond to data 120, which isgenerated by the decoder 106. The decoder metrics include a first metricand a second metric, such as a scale invariant metric (e.g., SER) and ascale variant metric (e.g., EM).

The data is classified into a first category if the data fails an errordetection check, into a second category if the data passes the errordetection check and is determined to be unreliable, or into a thirdcategory if the data passes the error detection check and is determinedto be reliable, at 604. For example, the classifier 108 of FIGS. 1 and 2may classify the data 120 into the first category if the data 120 failsan error detection check. The classifier 108 of FIGS. 1 and 2 mayclassify the data 120 into the second category if the data 120 passesthe error detection check and is determined to be unreliable. Theclassifier 108 of FIGS. 1 and 2 may also classify the data 120 into thethird category if the data 120 passes the error detection check and isdetermined to be reliable. A reliability of the data is determined basedon the decoder metrics and a metric threshold. For example, areliability of the data 120 in FIG. 2 may be determined based on thedecoder metrics 124 and a metric threshold.

The method 600 of FIG. 6 may be implemented by an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA)device, a processing unit such as a central processing unit (CPU), adigital signal processor (DSP), a controller, another hardware device, afirmware device, or any combination thereof. As an example, the methodof FIG. 6 can be performed by or in response to signals or commands froma processor that executes instructions, as described with respect toFIG. 6.

Referring to FIG. 7, a particular illustrative embodiment of a method ofdata classification for use in a wireless communication system isillustrated. The method 700 includes obtaining decoder metrics from adecoder, at 702. For example, the logic 116 of FIGS. 1 and 2 may obtainthe decoder metrics 124 from the decoder 106. The decoder metricscorrespond to data generated by the decoder. For example, the decodermetrics 124 of FIGS. 1 and 2 may correspond to data 120, which isgenerated by the decoder 106. The decoder metrics include an SER and anEM.

The data is classified into a first category if the data fails a cyclicredundancy check (CRC) check, into a second category if the data passesthe CRC check and is determined to be unreliable, or into a thirdcategory if the data passes the CRC check and is determined to bereliable, at 704. For example, the classifier 108 of FIG. 2 may classifythe data 120 into a fail category if the data 120 fails a cyclicredundancy check (CRC) check. The classifier 108 of FIG. 2 may classifythe data 120 into a false CRC pass category if the data 120 passes theCRC check and is determined to be unreliable. The classifier 108 of FIG.2 may also classify the data 120 into a pass category if the data 120passes the CRC check and is determined to be reliable. A reliability ofthe data is determined based on the decoder metrics and an EM threshold.For example, a reliability of the data 120 in FIG. 2 may be determinedbased on the decoder metrics 124 and an EM threshold.

The method 700 of FIG. 7 may be implemented by a processing unit such asa central processing unit (CPU), a digital signal processor (DSP), acontroller, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA) device, another hardware device, afirmware device, or any combination thereof. As an example, the methodof FIG. 7 can be performed by or in response to signals or commands froma processor that executes instructions, as described with respect toFIG. 6.

Referring to FIG. 8, a block diagram of a particular illustrativeembodiment of a wireless communication device is depicted and generallydesignated 800. The device 800 includes a processor 810, such as adigital signal processor (DSP), coupled to a memory 832. The wirelesscommunication device 800 may include a false alarm detector 868. Thefalse alarm detector 868 includes a decoder 862, a classifier 864, and athreshold updater 870, such as the decoder 106, the classifier 108, andthe threshold updater 210 of FIG. 2. In an illustrative embodiment, thefalse alarm detector 868 may correspond to the device, 104 of FIG. 1 orof FIG. 2, or may operate according to one or more of the methods ofFIG. 3, FIG. 6, or FIG. 7, or any combination thereof. Although thefalse alarm detector 868 is illustrated as integrated within theprocessor 810, in another embodiment, the device for data classificationfor use in a wireless communication system 868 may be external to theprocessor 810 and coupled to the processor 810.

At least a portion of the false alarm detector 868 may be implemented asinstructions executing, at the processor 810. For example, the memory832 may be a non-transitory computer readable medium storingcomputer-executable instructions 856 that are executable by theprocessor 810 (e.g. a computer) to cause the processor unit 810 toobtain decoder metrics from a decoder, where the decoder metricscorrespond to data generated by the decoder and where the decodermetrics include a symbol error rate (SER) and an energy metric (EM).Additionally, the computer-executable instructions 856 may includeinstructions that are executable by the processor unit 810 to cause theprocessor 810 to classify the data into a first category if the datafails a cyclic redundancy check (CRC) check, into a second category ifthe data passes the CRC check and is determined to be unreliable, orinto a third category if the data passes the CRC check and is determinedto be reliable, where a reliability of the data is determined based onthe decoder metrics and an EM threshold.

In addition, the memory 832 may be a non-transitory computer readablemedium storing computer-executable instructions 856 that are executableby the processor 810 (e.g. a computer) to cause the processor unit 810to obtain decoder metrics from a decoder, where the decoder metricscorrespond to data generated by the decoder and where the decodermetrics include a first metric and a second metric. Further, thecomputer-executable instructions 856 may include instructions that areexecutable by the processor 810 to cause the processor 810 to classifythe data into a first category if the data fails an error detectioncheck, into a second category if the data passes the error detectioncheck and is determined to be unreliable, or into a third category ifthe data passes the error detection check and is determined to bereliable, where a reliability of the data is determined based on thedecoder metrics and a threshold value.

FIG. 8 also shows a display controller 826 that is coupled to thedigital signal processor 810 and to a display 828. A coder/decoder(CODEC) 834 can also be coupled to the digital signal processor 810. Aspeaker 836 and a microphone 838 can be coupled to the CODEC 834.

FIG. 8 also indicates that a wireless controller 840 can be coupled tothe processor 810 and to a wireless antenna 842. In a particularembodiment, the processor 810, the display controller 826, the memory832, the CODEC 834, and the wireless controller 840 are included in asystem-in-package or system-on-chip device 822. In a particularembodiment, an input device 830 and a power supply 844 are coupled tothe system-on-chip device 822. Moreover, in a particular embodiment, asillustrated in FIG. 8, the display 828, the input device 830, thespeaker 836, the microphone 838, the wireless antenna 842, and the powersupply 844 are external to the system-on-chip device 822. However, eachof the display 828, the input device 830, the speaker 836, themicrophone 838, the wireless antenna 842, and the power supply 844 canbe coupled to a component of the system-on-chip device 822, such as aninterface or a controller.

While FIG. 8 illustrates a particular embodiment of a wireless device800 including false alarm detector 868, in other embodiments, the falsealarm detector 868 may be integrated in other electronic devicesincluding a set top box, a music player, a video player, anentertainment unit, a navigation device, a communications device, apersonal digital assistant (PDA), a fixed location data unit, and acomputer.

In conjunction with described embodiments, a system is disclosed thatincludes means for obtaining decoder metrics from the decoder, where thedecoder metrics correspond to data generated by a decoder and where thedecoder metrics include a symbol error rate (SER) and an energy metric(EM). For example, the means for obtaining decoder metrics from adecoder may include the logic 116 of FIG. 1, one or more other devicesor circuits configured to obtain decoder metrics from a decoder, or anycombination thereof.

The system may also includes means for classifying data into a firstcategory if the data fails a cyclic redundancy check (CRC) check, into asecond category if the data passes the CRC check and is determined to beunreliable, or into a third category if the data passes the CRC checkand is determined to be reliable, where a reliability of the data isdetermined based on the decoder metrics and an EM threshold. Forexample, the means for classifying data may include the classifier 108of FIG. 1, the classifier 108 of FIG. 2, one or more other devices orcircuits configured to classify data, or any combination thereof.

In conjunction with described embodiments, a system is disclosed thatincludes means for obtaining decoder metrics from the decoder, where thedecoder metrics correspond to data generated by a decoder and where thedecoder metrics include a first metric and a second metric. For example,the means for obtaining decoder metrics from a decoder may include thelogic 116 of FIG. 1, one or more other devices or circuits configured toobtain decoder metrics from a decoder, or any combination thereof.

The system may also includes means for classifying data into a firstcategory if the data fails an error detection check, into a secondcategory if the data passes the error detection check and is determinedto be unreliable, or into a third category if the data passes the errordetection check and is determined to be reliable, where a reliability ofthe data is determined based on the decoder metrics and a metricthreshold. For example, the means for classifying data may include theclassifier 108 of FIG. 1, the classifier 108 of FIG. 2, one or moreother devices or circuits configured to classify data, or anycombination thereof.

Those of skill would further appreciate that the various illustrativelogical blocks, configurations, modules, circuits, and algorithm stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software executed by aprocessor, or combinations of both. Various illustrative components,blocks, configurations, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or processor executableinstructions depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentdisclosure.

The steps of a method or algorithm described in connection with theembodiments disclosed herein may be embodied directly in hardware, in asoftware module executed by a processor, or in a combination of the two.A software module may reside in random access memory (RAM), flashmemory, read-only memory (ROM), programmable read-only memory (PROM),erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), registers, hard disk, aremovable disk, a compact disc read-only memory (CD-ROM), or any otherform of non-transient storage medium known in the art. An exemplarystorage medium is coupled to the processor such that the processor canread information from, and write information to, the storage medium. Inthe alternative, the storage medium may be integral to the processor.The processor and the storage medium may reside in anapplication-specific integrated circuit (ASIC). The ASIC may reside in acomputing device or a user terminal. In the alternative, the processorand the storage medium may reside as discrete components in a computingdevice or user terminal

The previous description of the disclosed embodiments is provided toenable a person skilled in the art to make or use the disclosedembodiments. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the principles defined hereinmay be applied to other embodiments without departing from the scope ofthe disclosure. Thus, the present disclosure is not intended to belimited to the embodiments shown herein but is to be accorded the widestscope possible consistent with the principles and novel features asdefined by the following claims.

1. A method of data classification for use in a wireless communicationsystem, the method comprising: obtaining decoder metrics from a decoder,wherein the decoder metrics correspond to data generated by the decoderand wherein the decoder metrics include a first metric and a secondmetric; and classifying the data into a first category if the data failsan error detection check, into a second category if the data passes theerror detection check and is determined to be unreliable, or into athird category if the data passes the error detection check and isdetermined to be reliable, wherein a reliability of the data isdetermined based on at least one of the decoder metrics and a threshold.2. The method of claim 1, wherein the data is decoded from a signalcarried on an enhanced data channel-absolute grant channel (E-AGCH). 3.The method of claim 2, wherein the signal is carried in an E-AGCH frame.4. The method of claim 2, wherein the signal is carried in an E-AGCHsub-frame.
 5. The method of claim 2, wherein the E-AGCH channel iscompliant with high-speed uplink packet access (HSUPA) protocol.
 6. Themethod of claim 2, wherein the error detection check is performed basedon a cyclic redundancy check (CRC) code.
 7. The method of claim 6,wherein the CRC code is a 16-bit CRC code.
 8. The method of claim 2,wherein the first metric is a scale invariant metric and the secondmetric is a scale variant metric.
 9. The method of claim 8, wherein thedata is determined to be unreliable if a function of the first metric,the second metric, and an adaptive scaling factor satisfies a criterion.10. The method of claim 9, wherein the function of the first metric, thesecond metric, and the adaptive scaling factor satisfies the criterionif the first metric and of the second metric divided by the adaptivescaling factor corresponds to a region of unreliable decode.
 11. Themethod of claim 10, further comprising updating the adaptive scalingfactor based on at least one of the decoder metrics.
 12. The method ofclaim 9, wherein the first metric includes a symbol error rate (SER) andthe second metric includes an energy metric (EM).
 13. The method ofclaim 9, wherein the decoder metrics and the data are associated with atransmission time interval (TTI).
 14. A device for data classificationfor use in a wireless communication system, the device comprising: logicto obtain decoder metrics from a decoder, wherein the decoder metricscorrespond to data generated by the decoder and wherein the decodermetrics include a first metric and a second metric; and a classifier toclassify the data into a first category if the data fails an errordetection check, to classify the data into a second category if the datapasses the error detection check and is determined to be unreliable, andto classify the data into a third category if the data passes the errordetection check and is determined to be reliable, wherein a reliabilityof the data is determined based on the decoder metrics and a threshold.15. The device of claim 14, wherein the data is decoded from a signalcarried on an enhanced data channel-absolute grant channel (E-AGCH). 16.The device of claim 15, wherein the E-AGCH channel is compliant withhigh-speed uplink packet access (HSUPA) protocol.
 17. The device ofclaim 15, further comprising a threshold updater to update the thresholdbased on the decoder metrics.
 18. The device of claim 15, wherein theerror detection check is performed based on a cyclic redundancy check(CRC) code.
 19. The device of claim 15, wherein the first metric is ascale variant metric and the second metric is a scale invariant metric.20. The device of claim 19, wherein the data is determined to beunreliable if a function of the first metric, the second metric, and ascaling factor satisfies a criterion.
 21. An apparatus for dataclassification for use in a wireless communication system, the apparatuscomprising: means for obtaining decoder metrics from a decoder, whereinthe decoder metrics correspond to data generated by the decoder andwherein the decoder metrics include a first metric and a second metric;and means for classifying the data into a first category if the datafails an error detection check, into a second category if the datapasses the error detection check and is determined to be unreliable, orinto a third category if the data passes the error detection check andis determined to be reliable, wherein a reliability of the data isdetermined based on the decoder metrics and a threshold.
 22. Theapparatus of claim 21, wherein the data is decoded from a signal carriedon an enhanced data channel-absolute grant channel (E-AGCH).
 23. Theapparatus of claim 22, further comprising means for updating thethreshold based on the decoder metrics.
 24. The apparatus of claim 22,wherein the data is determined to be unreliable if a function of thefirst metric, the second metric, and a scaling factor satisfies acriterion.
 25. A non-transitory processor-readable storage mediumcomprising instructions that, when executed by a processor, cause theprocessor to: obtain decoder metrics from a decoder, wherein the decodermetrics correspond to data generated by the decoder and wherein thedecoder metrics include a first metric and a second metric; and classifythe data into a first category if the data fails an error detectioncheck, into a second category if the data passes the error detectioncheck and is determined to be unreliable, or into a third category ifthe data passes the error detection check and is determined to bereliable, wherein a reliability of the data is determined based on thedecoder metrics and a threshold.
 26. The non-transitoryprocessor-readable storage medium of claim 25, wherein the data isdecoded from a signal carried on an enhanced data channel-absolute grantchannel (E-AGCH).
 27. The non-transitory processor-readable storagemedium of claim 26, wherein the data is determined to be unreliable if afunction of the first metric, the second metric, and a scaling factorsatisfies a criterion
 28. The non-transitory processor-readable storagemedium of claim 26, further comprising instructions that, when executedby the processor, cause the processor to update the threshold based onthe decoder metrics.